code: def make_pixel_mask(
      image: np.ndarray,
      output_size: Tuple[int, int],
      input_data_format: Optional[Union[str, ChannelDimension]] = None,
  ) -> np.ndarray:
      """
      Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
  
      Args:
          image (`np.ndarray`):
              Image to make the pixel mask for.
          output_size (`Tuple[int, int]`):
              Output size of the mask.
      """
      input_height, input_width = get_image_size(
          image, channel_dim=input_data_format
      )
      mask = np.zeros(output_size, dtype=np.int64)
      mask[:input_height, :input_width] = 1
      return mask
  
file_path: transformers/src/transformers/models/bridgetower/image_processing_bridgetower.py
imports: from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
  import numpy
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import PaddingMode, center_crop, pad, resize, to_channel_dimension_format
  from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_batched, is_scaled_image, to_numpy_array, valid_images
  from ...utils import TensorType, is_vision_available, logging
label_desc: Use image node_id 2 for calling a global function with example usage: make_pixel_mask(image, output_size, input_data_format) and returns: mask